Reconfigurable Intelligent Surfaces (RIS) has the capability to modify the wireless communication environment by altering the amplitude and phase of incoming electromagnetic waves, thereby enhancing system capacity and coverage. Precise channel state information (CSI) is crucial for achieving these benefits in RIS-enabled wireless communication systems. However, estimating the channels in RIS-Enabled systems poses significant challenges arises from the large number of RIS elements, which passively reflect signals and unavailability of signal processing capabilities. This article focuses on RIS-assisted multiple-input single-output (MISO) wireless communication systems and proposing a twofold-pattern sparsity orthogonal matching pursuit (TP-OMP) channel estimation (CE) method leveraging channel sparsity in the angular domain to reduce overhead. Monte Carlo (MC) simulations demonstrate a notable reduction in required pilot overhead compared to existing methods.